Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (4): 362-368.doi: 10.11896/jsjkx.210300032

• Information Security • Previous Articles     Next Articles

Study on Differential Privacy Protection for Medical Set-Valued Data

WANG Mei-shan, YAO Lan, GAO Fu-xiang, XU Jun-can   

  1. School of Computer Science and Engineering, Northeastern University, Shenyang 110169, China
  • Received:2021-03-02 Revised:2021-08-07 Published:2022-04-01
  • About author:WANG Mei-shan,born in 1996,postgraduate.Her main research interests include privacy protection and so on.GAO Fu-xiang,born in 1961,Ph.D,professor.His main research interests include computer network security,embedded computer networks.

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Abstract: Electronic medical data surges along with the constant development of information technologies and medical care digitalization.It provides foundations for further application on data analysis, data mining and intelligent diagnosis.The fact that me-dical data are massive and involve a lot of patient privacy.How to protect patient privacy while using medical data is challenging.The predominant principle for the solutions is anonymity.It is not competent in confidentiality or availability when attackers possess strong background knowledge.This paper proposes an optimized classification tree and an improved Diffpart.In our design, association of data is introduced to sift set-valued data for DP based perturbation, which satisfies the utility and supports statistic query.Then test is conducted with 240000 practical medical data and the results show that the proposed algorithm holds DP distribution and outperforms Diffpart in privacy and utility.

Key words: Data utility, Differential privacy, Medical big data, Privacy protection, Set-Valued data

CLC Number: 

  • TP309.2
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